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Performance of an Intelligent Messaging Tool for Clinical Communications.

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This summary is machine-generated.

The Smart Messaging Tool (SMT) significantly reduced the time to read high-acuity patient messages, improving care. This natural language processing solution enhances message management in healthcare systems.

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Area of Science:

  • Health Informatics
  • Natural Language Processing in Healthcare
  • Clinical Workflow Optimization

Background:

  • Rising patient portal message volumes can lead to care delays and clinician burnout.
  • Effective management of patient messages is crucial for timely and efficient healthcare delivery.
  • Existing systems struggle to cope with the increasing scale of patient communication.

Purpose of the Study:

  • To evaluate the effectiveness of the Smart Messaging Tool (SMT) in classifying patient messages.
  • To assess the SMT's viability within a large, integrated, value-based healthcare system.
  • To determine if SMT improves the processing of high-acuity patient communications.

Main Methods:

  • A quality improvement study comparing SMT implementation (2 years) with a legacy system.
  • SMT, a natural language processing tool, was deployed across primary care departments at SCPMG.
  • Median time to first read for high-acuity messages and SMT classification accuracy were measured.

Main Results:

  • Median time to first read for high-acuity messages decreased from 22.03 hours to 5.02 hours.
  • SMT achieved 81.0% classification accuracy, significantly outperforming the legacy system's 44.0%.
  • Top-3 and top-5 accuracy rates for SMT were 88.5% and 90.7%, respectively.

Conclusions:

  • SMT reliably categorizes patient messages, supporting timely processing of high-acuity communications.
  • The tool demonstrates practical applicability for organizations facing patient message management challenges.
  • Leveraging natural language processing offers a viable solution to optimize healthcare communication workflows.